knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)

library(tidyverse)
library(here)
library(sf)
library(tmap)

Read in the Data

sf_trees <- read_csv(here('data', 'sf_trees', 'sf_trees.csv'),
                     show_col_types = FALSE)

Part 1: Wrangling and ggplot review

Example 1: Find Counts of observation legal_status & wrangle a bit.

### method 1: group_by() %>% summarize()
sf_trees %>% 
  group_by(legal_status) %>% 
  summarize(tree_count = n())
## # A tibble: 10 × 2
##    legal_status                 tree_count
##    <chr>                             <int>
##  1 DPW Maintained                   141725
##  2 Landmark tree                        42
##  3 Permitted Site                    39732
##  4 Planning Code 138.1 required        971
##  5 Private                             163
##  6 Property Tree                       316
##  7 Section 143                         230
##  8 Significant Tree                   1648
##  9 Undocumented                       8106
## 10 <NA>                                 54
## method 2: different way plus a few new functions
top_5_status <- sf_trees %>% 
  count(legal_status) %>% # pretty much the same as using group_by and summarize
  drop_na(legal_status) %>% 
  rename(tree_count = n) %>% 
  relocate(tree_count) %>% 
  slice_max(tree_count, n= 5) %>%#slice_max() tells it to only take the top variables by within column specified and how many you want to keep i.e. n= X
  arrange(desc(tree_count)) #arrange reorganizes data, either high to low or opposite, desc() specifies high to low

Make graph of the top 5 from above:

ggplot(data= top_5_status, aes(x= fct_reorder(legal_status, tree_count), y= tree_count)) +
  geom_col(fill= 'darkgreen') +
  labs(x= 'Legal Status', y= 'Tree Count', title = 'Plot of Tree Counts by Legal Status') +
  coord_flip() +
  theme_minimal()

Example 2: Only going to keep observations where legal status is “Permitted Site” and caretaker is “MTA”, and store as permitted_data_df

shift-cmd-c to comment/uncomment quickly

# sf_trees$legal_status %>% unique()
permitted_data_df <- sf_trees %>% 
  filter(legal_status == 'Permitted Site', caretaker == 'MTA')

Example 3 Only keep Blackwood Acacia Trees, and then only keep columns legal_status, date, latitude, longitude, and store as blackwood_acacia_df

blackwood_acacia_df <- sf_trees %>% 
  filter(str_detect(species, 'Blackwood Acacia')) %>% ##str_detect tells it to look into the column specified i.e. "species" and then find the observations that match the description i.e. "Blackwood Acacia"
  select(legal_status, date, lat = latitude, long =longitude)

## Make a little graph of locations
ggplot(data= blackwood_acacia_df, aes(x= long, y= lat)) +
  geom_point(color= 'darkgreen')

Example 4 Use tidyr::separate()

## Separate function helps to take apart two parts of a column by a specifying where you want to separate from, in this instance we use ' :: ' to tell it to break apart the scientific and common names by finding the ::
sf_trees_sep <- sf_trees %>% 
  separate(species, into = c('spp_scientific', 'spp_common'), sep = ' :: ')

Example 5 use `tidyr::unite() allows you to combine two columns

ex_5 <- sf_trees %>% 
  unite('id_status', tree_id, legal_status, sep = '_cool_')

Part 2: Make some maps

Step 1 convert the lat/long to spatial point, st_as_sf()

blackwood_acacia_sf <- blackwood_acacia_df %>% 
  drop_na(lat, long) %>% 
  st_as_sf(coords = c('long', 'lat'))

## we need to tell r what the coordinate reference system is
st_crs(blackwood_acacia_sf) <- 4326

ggplot(data = blackwood_acacia_sf) +
  geom_sf(color = 'blue') +
  theme_minimal()

Read in the SF shapefile and add to map

sf_map <- read_sf(here('data', 'sf_map', 'tl_2017_06075_roads.shp'))

sf_map_transform <- st_transform(sf_map, 4326)

ggplot(data= sf_map_transform) +
  geom_sf()

Combine the maps!

ggplot() +
  geom_sf(data= sf_map,
          size = .1,
          color = 'darkgrey') +
  geom_sf(data = blackwood_acacia_sf,
          color = 'red',
          size =0.5) +
  theme_void() +
  labs(title =  'Blackwood acacias in SF')

Now an interactive map!

tmap_mode('view')

tm_shape(blackwood_acacia_sf) +
  tm_dots()